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Review
. 2021 Oct 15;14(20):6135.
doi: 10.3390/ma14206135.

A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

Suria Devi Vijaya Kumar  1 , Michael Lo Yin Kai  1 , Thibankumar Arumugam  1 , Saravanan Karuppanan  1
Affiliations
Review

A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

Suria Devi Vijaya Kumar et al. Materials (Basel). .

Abstract

This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results.

Keywords: artificial neural network; failure pressure prediction; finite element analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A traditional feedforward neural network (FFNN) model with two hidden layers.
Figure 2
Figure 2
Development of FEM [56].
See this image and copyright information in PMC

References

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